The present invention relates to an object position correction apparatus for use in displaying a position of an observation subject for the user, and an object position correction method and an object position correction program for such an apparatus.
A camera is sometimes used as a sensor capable of detecting a position of an object.
The camera is not capable of providing 100% in the ID identification precision of an object (since the ID of the object is recognized from image characteristics (shape, color, or the like) obtained from the camera). Even in a case where an identification result through the camera indicates an object A, actually, an object (object B, or object C) other than the object A might be identified. In such a case, for example, the object identified by the camera is represented as having a probability of being the object A being 80%, having a probability of being the object B being 10%, and having a probability of being the object C being 10%. Moreover, the identification rate of objects having similar image characteristics becomes lower.
For example, objects, such as a tomato and an apple, or the like, having colors or shapes being similar to each other are very difficult to be identified from each other with high precision. Moreover, although slightly different depending on the function or layout of a camera, a certain degree of error is normally contained in the observation position (the result of a measured position). The identification ID and observation position of an object are collectively referred to as observed values.
Conventionally, there has been proposed a technique has been proposed by combining a plurality of observed values of a sensor having ambiguous identification ID's or observed positions of articles with one another, the position of an object is estimated by the framework of Bayesian estimation on a probability basis, while compensating for insufficiency in observation precision (Non-Patent Document 1).
In Non-Patent Document 1, however, even a slight probability (such a probability that the object identified by the camera corresponds to the object B in the above-mentioned example) is utilized in processes for estimating the object position, the estimation results tend to be influenced by observed values of the ether sensors. FIG. 19 shows an example of this state. As the result of an object identification, observed value 1 includes a probability of being an object A being 90%, and a probability of being an object B being 10%. As the result of an object identification, observed value 2 includes a probability of being an object A being 10%, and a probability of being an object B being 90%. In these observed states, upon estimation of the position, the estimated position of the object A is slightly influenced by the observed value 2, with the result that the observed position has a slight positional deviation in a direction toward the observed value 2 from the position of the observed value 1 (the detailed description of the object position estimation will be given later). Since the observed value 2 has a possibility that it is obtained by observing the object A, the estimation result of FIG. 19 is probabilistically correct. However, the estimated position with the positional deviation (for example, the average value in the Gauss's distribution) tends to form a position that gives a visually uncomfortable impression to the user. For example, the following examples are given: in a case where the observed object is a car, the estimated position of the car does not exist on a road, or in a case where the observed object is a person, the estimated position of the person is on a table.
As a technique for correcting the deviation in the estimated position, a technique using a map matching has been proposed (Patent Document 1). In this method, since vehicle position information acquired by GPS (Global Positioning System) contains an error, information to be given to the user is flexibly altered by using a map matching technique based on outputs from an axel sensor, a brake sensor, and a blinker sensor.
Prior Art Document
Patent Document
    Patent Document 1: Japanese Unexamined Patent Publication No. 11-271073Non-Patent Document    Non-Patent Document 1: Hirofumi Kanazaki, Takehisa Yairi, Kazuo Machida, Kenji Kondo, Yoshihiko Matsukawa, “Variational Approximation Data Association Filter”, 15th European Signal Processing Conference (EUSIPCO2007).